code: def rmse(y_label, y_predict):
      """
      Calculate square root of the mean squared error (RMSE).
  
      .. math::
  
          \\text{RMSE} = \\sqrt{(\\frac{1}{n}\\sum_{t=1}^n (y_t-\\hat{y_t})^2)}
  
      :param y_label: Array-like of shape = (n_samples, \*).
             Ground truth (correct) target values.
      :param y_predict: Array-like of shape = (n_samples, \*).
             Estimated target values.
      :return: Ndarray of floats.
               An array of non-negative floating point values (the best value is 0.0).
      """
      return np.sqrt(mse(y_label, y_predict))
  
file_path: BigDL/python/chronos/src/bigdl/chronos/metric/forecast_metrics.py
imports: from numpy import ndarray
  import numpy
  from bigdl.nano.utils.common import invalidInputError
  from timeit import repeat
  import random
label_desc: Use image node_id 3 for calling a global function with example usage: rmse(y_label, y_predict) and returns: np